Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!mnetor!seismo!columbia!rutgers!husc6!diamond.bbn.com!aweinste From: aweinste@Diamond.BBN.COM (Anders Weinstein) Newsgroups: comp.ai,comp.cog-eng Subject: Re: The symbol grounding problem Message-ID: <6521@diamond.BBN.COM> Date: Thu, 11-Jun-87 17:15:31 EDT Article-I.D.: diamond.6521 Posted: Thu Jun 11 17:15:31 1987 Date-Received: Sat, 20-Jun-87 12:39:42 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> <6453@diamond.BBN.COM> <828@mind.UUCP> Reply-To: aweinste@Diamond.BBN.COM (Anders Weinstein) Organization: BBN Laboratories, Inc., Cambridge, MA Lines: 97 Keywords: icons, categories, symbols, grounding, modularity, cognition Summary: "Grounding" reduces to behavior, so that's where the real issue is. Xref: mnetor comp.ai:542 comp.cog-eng:129 In article <828@mind.UUCP> Stevan Harnad writes > >> There's no [symbol] grounding problem, just the old >> behavior-generating problem > There is: >(1) the behavior-generating problem (what I have referred to as the problem of >devising a candidate that will pass the Total Turing Test), (2) the >symbol-grounding problem (the problem of how to make formal symbols >intrinsically meaningful, independent of our interpretations), and (3) >the conjecture (based on the existing empirical evidence and on >logical and methodological considerations) that (2) is responsible for >the failure of the top-down symbolic approach to solve (1). It seems to me that in different places, you are arguing the relation between (1) and (2) in both directions, claiming both (A) The symbols in a purely symbolic system will always be ungrounded because such systems can't generate real performance; and (B) A purely symbolic system can't generate real performance because its symbols will always be ungrounded. That is, when I ask you why you think the symbolic approach won't work, one of your reasons is always "because it can't solve the grounding problem", but when I press you for why the symbolic approach can't solve the grounding problem, it always turns out to be "because I think it won't work." I think we should get straight on the priority here. It seems to me that, contra (3), thesis (A) is the one that makes perfect sense -- in fact, it's what I thought you were saying. I just don't understand (B) at all. To elaborate: I presume the "symbol-grounding" problem is a *philosophical* question: what gives formal symbols original intentionality? I suppose the only answer anybody knows is, in brief, that the symbols must be playing a certain role in what Dennett calls an "intentional system", that is, a system which is capable of producing complex, adaptive behavior in a rational way. Since such a system must be able to respond to changes in its environment, this answer has the interesting consequence that causal interaction with the world is a *necessary* condition for original intentionality. It tells us that symbols in a disconnected computer, without sense organs, could never be "grounded" or intrinsically meaningful. But those in a machine that can sense and react could be, provided the machine exhibited the requisite rationality. And this, as far as I can tell, is the end of what we learn from the "symbol grounding" problem -- you've got to have sense organs. For a system that is not causally isolated from the environment, the symbol-grounding problem now just reduces to the old behavior-generating problem, for, if we could just produce the behavior, there would be no question of the intentionality of the symbols. In other words, once we've wised up enough to recognize that we must include sensory systems (as symbolic AI has), we have completely disposed of the "symbol grounding" problem, and all that's left to worry about is the question of what kind of system can produce the requisite intelligent behavior. That is, all that's left is the old behavior-generating problem. Now as I've indicated, I think it's perfectly reasonable to suspect that the symbolic approach is insufficient to produce full human performance. You really don't have to issue any polemics on this point to me; such a suspicion could well be justified by pointing out the triviality of AI's performance achievements to date. What I *don't* see is any more "principled" or "logical" or "methodological" reason for such a suspicion; in particular, I don't understand how (B) could provide such a reason. My system can't produce intelligent performance because it doesn't make its symbols meaningful? This statement has just got things backwards -- if I could produce the behavior, you'd have to admit that its symbols had all the "grounding" they needed for original intentionality. In sum, apart from the considerations that require causal embedding, I don't see that there *is* any "symbol-grounding" problem, at least not any problem that is any different from the old "total-performance generating" problem. For this reason, I think your animadversions on symbol grounding are largely irrelevant to your position -- the really substantial claims pertain only to "what looks like it's likely to work" for generating intelligent behavior. On a more specific issue: > >You've bypassed the three points I brought up in replying to your >challenge to my invertibility criterion for an analog transform the >last time: (1) the quantization in standard A/D is noninvertible, Yes, but *my* point has been that since there isn't necessarily any more loss here than there is in a typical A/A transformation, the "degree of invertibility" criterion cross-cuts the intuitive A/D distinction. Look, suppose we had a digitized image, A, which is of much higher resolution than another analog one, B. A is more invertible since it contains more detail from which to reconstruct the original signal, but B is more "shape-preserving" in an intuitive sense. So, which do you regard as "more analog"? Which does your theory think is better suited to subserving our categorization performance? If you say B, then invertibility is just not what you're after. Anders Weinstein BBN Labs